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This lecture series focus on the fundamentals of intelligence and ML methodologies that can be utilized to solve engineering problems. In the first lecture, you will build up the basic skills needed to develop intelligent solutions for pattern recognition in experimental/numerical datasets, model abstractions, optimization and process control. Another objective is to establish a strong scientific background in order to clarify the current capabilities, challenges and opportunities in ML. The course includes weekly software sessions, organized as jupyter notebooks for hands-on experience and finalizes with an End-to-End Machine Learning Project.
At the end of the lecture period, we are organizing best project contest, $`(KI)^2T`$, during which the finalists present their work. The best is decided by the votes of the audience! Feel free to explore these projects, which are available in the "Hall of Fame".
At the end of the lecture period, we organize a project contest, $`(KI)^2T`$, during which the finalists picked by the DDE Team present their work. The best is decided by the votes of the audience! Feel free to explore these projects, which are available in the ["Hall of Fame"](/Hall_of_Fame).
[![image](others/images/ml2.png)](https://www.its.kit.edu/Lehrveranstaltungen_DataDrivenEngineering_II.php)
In the second lecture, we dive into the details of the most recent applications in data driven engineering within the scope of machine learning (ML). Building upon the skills developed in the “Data Driven Engineering 1: Machine Learning for Dynamical Systems” course, you will learn about complex model architectures through different “themes”, with the objective of providing a deeper background and capability to navigate through the recent developments in the field.
In the second lecture, we dive into the details of the most recent applications in data driven engineering within the scope of machine learning. Building upon the skills developed in the first course, we visit complex model architectures through different “themes”, with the objective of providing a deeper background and capability to navigate through the recent developments in the field. The [content](/DDE_II_Advanced_Topics) is updated every year, reflecting the recent developments in the field.
The second lecture is also integrated with group projects. Here groups of students are assigned open-ended research problems with scientific mentors from ITS at the beginning of the semester. In the following weeks, the groups work on their problems by combining the fundamental skills they gained in the first and second lectures. The progress will be monitored via project sessions throughout the semester. The finalized work will be presented at the end of the semester and published in the lecture repository.
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